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Record W2911048450 · doi:10.1115/imece2018-86689

Computational Study of Onset Dynamics in Neuron-Spiking With Threshold Adaptation

2018· article· en· W2911048450 on OpenAlexaff
Loabat S. Kavan, Abhijeet Wadkar, Samuel F. Asokanthan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
Topicstochastic dynamics and bifurcation
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceImpulse (physics)WaveformElectromagnetic fieldNeuroscienceComputational modelImpulse responseVoltagePhysicsControl theory (sociology)Statistical physicsSimulationArtificial intelligenceMathematicsPsychology

Abstract

fetched live from OpenAlex

Magnetic Seizure therapy (MST) is emerging as a treatment for patients suffering from severe depression where an induced current due to an external electromagnetic field is employed. This procedure can only be considered effective when sufficient induced current activates the neurons in the prefrontal cortex. Computer simulation of MST is essential to provide better insight of this procedure and to supplement the clinical trials. To this end, an understanding of transmission of electric impulse through the nerve is considered essential. Stochastic impulse spike sequences are trigged when membrane potential crosses a threshold value. Quantitative numerical predictions employing a mathematical model and induced current defined via Ornstein Uhlenbeck (OU) process predict that both the linear steady-state and rectified models provide adequate threshold adaptation while the rectified model exhibits superior spiking behavior. The present study when combined with suitable numerical simulation of electromagnetic induction is envisaged to aid the MST clinical treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.247
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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